Tailoring recommendation algorithms to ideal preferences makes users better off

People often struggle to do what they ideally want because of a conflict between their actual and ideal preferences. By focusing on maximizing engagement, recommendation algorithms appear to be exacerbating this struggle. However, this need not be the case. Here we show that tailoring recommendation algorithms to ideal (vs. actual) preferences would provide meaningful benefits to both users and companies. To examine this, we built algorithmic recommendation systems that generated real-time, personalized recommendations tailored to either a person’s actual or ideal preferences. Then, in a high-powered, pre-registered experiment (n = 6488), we measured the effects of these recommendation algorithms. We found that targeting ideal rather than actual preferences resulted in somewhat fewer clicks, but it also increased the extent to which people felt better off and that their time was well spent. Moreover, of note to companies, targeting ideal preferences increased users' willingness to pay for the service, the extent to which they felt the company had their best interest at heart, and their likelihood of using the service again. Our results suggest that users and companies would be better off if recommendation algorithms learned what each person was striving for and nudged individuals toward their own unique ideals.


Why Actual-ideal Preference Discrepancies Emerge Distinction Between Actual-ideal Preference Discrepancies and Related Constructs
Timing Analyses    Table S1. Summary statistics for all conditions and all participants.  Table S3. Summary statistics and significance tests comparing the actual and ideal conditions for participants with a preference discrepancy. Table S4. Regression results comparing the actual and ideal conditions for participants with a preference discrepancy with controls. Table S5. Summary statistics and significance tests for post-reading reactions of participants who chose to read the recommended article. Table S6. Summary statistics and significance tests for post-reading reactions of participants who chose not to read the recommended article. Table S7. Interaction of condition and choice to read recommended article on post-reading dependent variables.
The second question concerns why one wouldn't simply decide to bring actual attitudes in line with their ideal attitudes. Research shows that people do, in fact, act in ways that facilitate this alignment (DeMarree, et al., 2017;Vaughan-Johnston, Fabrigar, Xia, DeMarree, & Clark, 2023), but there are obstacles to this in the form of reality constraints and structural constraints (Wheeler & DeMarree, 2019). In brief, reality constraints concern limitations on one's ability to completely shape their experiential reactions to objects. Although one can learn to like things one initially dislikes (e.g., exercise) and vice versa, not all experiences are amenable to easy manipulation, despite the use of epistemic and teleologic tactics such as reinterpretation or suppression (see Maio & Thomas, 2007). Sometimes one's experienced responses to a stimulus (e.g., the taste of liver) are resistant to reformulation or change. Structural constraints concern the array of cognitive components (e.g., beliefs, identities, values, motivations, etc.) that are mentally associated with the ideal attitude in question. These can include not just one's own cognitive components but also those of others (i.e., interpersonal congruence). Because ideal attitudes exist in a potentially vast network of associated components, not all of which may themselves be consistent, achieving perfect consonance between all components in the network may be impossible.

Distinction Between Actual-ideal Preference Discrepancies and Related Constructs
An individual's ideal preferences can be more normatively virtuous than one's actual preferences (e.g., wanting to like exercising more than one actually does), less normatively virtuous that one's actual preferences (e.g., wanting to like scotch more than one actually does), or unrelated to virtue altogether (e.g., wanting to like one's apartment more than one actually does). As these examples illustrate, the actual-ideal preference distinction differs from those of related constructs. For example, though research on actual-ideal preference discrepancies shares terminology with the ideal selves of regulatory focus theory (Higgins, 2011), actual-ideal preference discrepancies need have nothing to do with approach vs. avoidance orientations, goal pursuit, or the self-concept. Some actual-ideal preference discrepancies stem from strictly pragmatic concerns (e.g., wanting to like things simply because they are popular, or wanting to like things that one owns or uses, as in the case of the apartment example above). For similar reasons, they are distinct from want-should conflicts (Milkman et al., 2008). Actual-ideal preference discrepancies also need not involve any conflict between short-term and long-term desires, which makes them distinct from intertemporal tradeoffs (Frederick et al., 2003).
The reader may question why we did not also study ought attitudes, that is, the attitudes one feels one ought to hold. There are two reasons for this: First, ideal attitudes have been shown to be stronger predictors of behavior (DeMarree et al., 2017), perhaps because they reflect personal, rather than interpersonal standards for behavior. Second, showing effects of recommending content aligned with one's individually determined ideal attitudes (vs. one's socially determined ought attitudes) would illustrate the benefit of truly personalizing content to an individual's desired preferences, rather than to normative societal prescriptions.

Timing Analyses
We ran additional analyses to examine how long participants spent reading the recommended article. Because the reading time measure was skewed, we log-transformed this measure for the following analyses. However, the statistical conclusions remain unchanged when using the untransformed measure. We began by comparing reading time between the actual and ideal conditions. Because the personalized recommendations for each condition may have differed in article length and other ways, we used several different approaches to account for differences in articles. First, we ran a mixed-effects linear model in which we regressed log- In addition to comparing condition, we also examined the relationship between the predicted degree of discrepancy that participants felt toward the recommended article and logtransformed reading time. Because the machine learning models generated point predictions for how much each person actually and ideally wanted to read the recommended article, we could calculate the difference between these values to serve as an estimate of how much people experienced tension between their actual and ideal preferences for the recommended article. This allowed for a more granular approach than using condition alone. For example, even for participants in the same condition, some participants could have a bigger predicted discrepancy than others for a particular article, and this presumably greater tension could perhaps affect reading time. Conducting similar analyses as before, we ran a mixed-effects linear model in which we regressed log-transformed reading time on predicted preference discrepancy with a random intercept for the recommended article; there was no statistically significant effect of predicted preference discrepancy on reading time (b = 0.00, 95% CI = [-0.00, 0.01], t(1779) = 0.59, p = .555). When we ran a linear model in which we regressed log-transformed reading time on predicted preference discrepancy and word count, controlling for the interaction between the two, there was no statistically significant effect of predicted preference discrepancy on reading time (b = 0.00, 95% CI = [-0.00, 0.01], t(2500) = 0.57, p = .566), and the interaction was not significant either (b = -0.00, 95% CI = [-0.00, 0.00], t(2500) = -0.60, p = .546).
In addition, we ran analyses to examine how long it took participants to decide whether to read the recommended article. We used log-transformed choice time to correct for skew in the following analyses; however, as before, the statistical conclusions remain unchanged when using the untransformed measure. We began by comparing choice time between the actual and ideal conditions. We used a mixed-effects linear model to regress log-transformed choice time on condition with a random intercept for the article and found no significant differences between conditions (b = 0.01, 95% CI = [-0.03, 0.05], t(2502) = 0.63, p = .529). In addition to comparing condition, we also examined the relationship between the predicted degree of discrepancy that participants felt toward the recommended article and log-transformed choice time. We ran a mixed-effects linear model in which we regressed log-transformed choice time on predicted preference discrepancy with a random intercept for the recommended article; there was no statistically significant impact of predicted preference discrepancy on choice time (b = -0.00, 95% CI = [-0.00, 0.00], t(2502) = -1.32, p = .188).

Fig. S1
. True rank of article predicted to be highest (actual). This histogram depicts the true preference rank (out of 10) of the article that the algorithm predicted to be highest on actual preference. Predictions were generated using 10-fold cross validation.

Fig. S2. True rank of article predicted to be highest (ideal).
This histogram depicts the true preference rank (out of 10) of the article that the algorithm predicted to be highest on ideal preference. Predictions were generated using 10-fold cross validation.    Table S3. Summary statistics and significance tests comparing the actual and ideal conditions for participants with a preference discrepancy. Data include only participants with a preference discrepancy (i.e., those who would have received different recommendations in the actual and ideal conditions). For continuous measures, two-tailed Welch Two Sample t-tests were used to evaluate significance, and Cohen's d was used to estimate effect size. Although we pre-registered one-tailed t-tests to increase our statistical power (Lakens, n.d.), this table reports results from the more conservative two-tailed t-tests. For the sole dichotomous measure, choosing to read the recommended article, a logistic regression was used to evaluate significance, and the odds ratio (OR) was used to estimate effect size. We note that unlike the other positive effect sizes displayed, the effect of the actual condition was stronger than the effect of the ideal condition for this dependent variable, as evidenced by the odds ratio, which always takes on positive values, being less than 1.     Table S7. Interaction of condition and choice to read recommended article on post-reading dependent variables. Data only includes participants with a preference discrepancy.